{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-pathwaycom--llm-app","slug":"pathwaycom--llm-app","name":"llm-app","type":"template","url":"https://pathway.com/developers/templates/","page_url":"https://unfragile.ai/pathwaycom--llm-app","categories":["data-pipelines","rag-knowledge"],"tags":["chatbot","hugging-face","llm","llm-local","llm-prompting","llm-security","llmops","machine-learning","open-ai","pathway","rag","real-time","retrieval-augmented-generation","vector-database","vector-index"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-pathwaycom--llm-app__cap_0","uri":"capability://data.processing.analysis.real.time.multi.source.document.ingestion.with.live.synchronization","name":"real-time multi-source document ingestion with live synchronization","description":"Pathway's llm-app connects to and continuously monitors multiple heterogeneous data sources (Google Drive, SharePoint, S3, Kafka, PostgreSQL, file systems) using source-specific connectors that poll or stream changes. Documents are automatically detected, tracked for modifications, and re-indexed without manual intervention, enabling RAG systems to stay synchronized with upstream data without batch processing delays or stale context windows.","intents":["I want my RAG system to automatically pick up new documents from Google Drive and SharePoint without manual uploads","I need to index real-time data streams from Kafka or PostgreSQL into my LLM application","I want to ensure my knowledge base is always in sync with source-of-truth data systems"],"best_for":["Enterprise teams building knowledge bases from distributed data sources","Teams requiring live data freshness in RAG systems without batch ETL jobs","Organizations with multi-cloud or hybrid data architectures"],"limitations":["Connector availability varies by source — not all cloud storage providers have native connectors","Real-time sync adds operational complexity for managing connection credentials and monitoring connector health","Large-scale document changes (millions of files) may require tuning of polling intervals to avoid API rate limits"],"requires":["Pathway framework installed (Python 3.9+)","API credentials for target data sources (Google Drive API key, SharePoint tenant credentials, S3 access keys, etc.)","Network connectivity to source systems","Docker for containerized deployment of connectors"],"input_types":["file system paths","cloud storage URLs","database connection strings","Kafka topic names","API endpoints"],"output_types":["document metadata (path, modification time, source)","document content streams","change event logs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathwaycom--llm-app__cap_1","uri":"capability://data.processing.analysis.adaptive.document.chunking.and.embedding.with.configurable.text.splitting","name":"adaptive document chunking and embedding with configurable text splitting","description":"Pathway's llm-app provides configurable text splitting strategies (fixed-size chunks, semantic boundaries, sliding windows) that divide documents into appropriately-sized segments before embedding. The system supports multiple embedding models (OpenAI, Hugging Face, local models) and allows customization of chunk size, overlap, and splitting logic through app.yaml configuration, enabling optimization for different document types and retrieval patterns without code changes.","intents":["I want to chunk documents intelligently based on semantic boundaries rather than fixed token counts","I need to configure chunk size and overlap parameters for my specific domain (legal docs vs. code vs. research papers)","I want to use local embedding models instead of cloud APIs for privacy or cost reasons"],"best_for":["Teams building domain-specific RAG systems with heterogeneous document types","Organizations with privacy requirements preventing cloud embedding API usage","Developers optimizing retrieval quality through chunk size experimentation"],"limitations":["Semantic chunking (e.g., sentence-boundary aware) requires language-specific tokenizers and adds ~50-200ms per document","Local embedding models require GPU resources for reasonable throughput; CPU-only inference is slow for large document collections","No built-in adaptive chunking based on document structure (e.g., respecting code block boundaries) — requires custom splitting logic"],"requires":["Pathway framework with embedding module","Python 3.9+","For cloud embeddings: API key for OpenAI or Hugging Face Inference API","For local embeddings: GPU (CUDA/Metal) or CPU with 4GB+ RAM","app.yaml configuration file with text_splitter and embedding_model sections"],"input_types":["raw document text","parsed document content with metadata","document format specifications (PDF, DOCX, markdown)"],"output_types":["text chunks with metadata (source, chunk_id, position)","vector embeddings (float arrays, typically 384-1536 dimensions)","chunk-to-source mappings for retrieval traceability"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathwaycom--llm-app__cap_10","uri":"capability://automation.workflow.drive.alert.system.with.document.change.monitoring.and.notification","name":"drive alert system with document change monitoring and notification","description":"Pathway's specialized Drive Alert template monitors cloud storage (Google Drive, SharePoint) for document changes and generates alerts or notifications based on configurable rules (new documents, modifications, specific keywords). The system uses real-time connectors to detect changes, applies filtering logic, and triggers actions (email notifications, webhook calls, database updates) when conditions are met, enabling proactive monitoring of document repositories.","intents":["I want to be notified when new documents are added to shared drives","I need to monitor for documents containing specific keywords or matching patterns","I want to trigger workflows when documents are modified or deleted"],"best_for":["Teams managing shared document repositories with compliance requirements","Organizations needing real-time alerts on document changes","Compliance and audit teams monitoring document repositories for policy violations"],"limitations":["Real-time monitoring adds operational overhead for managing connector health and API rate limits","Notification delivery is not guaranteed; webhook failures or email delivery issues may cause missed alerts","Filtering logic (keyword matching, pattern detection) requires careful tuning to avoid false positives","No built-in deduplication; rapid document changes may trigger duplicate alerts","Scaling to monitor thousands of documents requires careful connector configuration to avoid API rate limiting"],"requires":["Pathway framework with Drive Alert template","Cloud storage API credentials (Google Drive API, SharePoint API)","Python 3.9+","Notification backend (email service, webhook endpoint, Slack API, etc.)","Configuration specifying monitoring rules and alert actions in app.yaml"],"input_types":["cloud storage paths or folders to monitor","alert rules (keyword patterns, document types, change types)","notification configuration (email addresses, webhook URLs, Slack channels)"],"output_types":["change events (document added, modified, deleted)","alert notifications (email, webhook, Slack message)","change logs and audit trails","alert metadata (timestamp, rule matched, document details)"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathwaycom--llm-app__cap_11","uri":"capability://planning.reasoning.langgraph.agent.integration.with.tool.calling.and.multi.step.reasoning","name":"langgraph agent integration with tool-calling and multi-step reasoning","description":"Pathway's llm-app integrates with LangGraph to enable agentic workflows where LLMs can call tools (retrieve documents, execute code, query databases) and reason over multiple steps. The integration allows Pathway RAG pipelines to be used as tools within LangGraph agents, enabling complex multi-step reasoning tasks (research synthesis, code generation with context, multi-document analysis) while maintaining real-time data freshness from Pathway's streaming indices.","intents":["I want to build an agent that retrieves documents, analyzes them, and takes actions based on findings","I need multi-step reasoning where an agent can retrieve context, generate code, and execute it","I want to combine RAG with tool-calling to enable complex research or analysis workflows"],"best_for":["Teams building autonomous agents that combine reasoning with information retrieval","Applications requiring multi-step workflows (research, analysis, code generation)","Developers integrating Pathway RAG into LangGraph-based agent systems"],"limitations":["Agent reasoning adds multiple LLM calls per task; cost and latency scale with reasoning steps","Tool-calling requires careful prompt engineering to ensure agents select appropriate tools","No built-in error recovery; agent failures (hallucinated tool calls, infinite loops) require manual intervention","Debugging multi-step agent workflows is complex; tracing reasoning steps requires detailed logging","Integration with LangGraph requires familiarity with both Pathway and LangGraph APIs"],"requires":["Pathway framework with RAG pipeline","LangGraph library (Python)","LLM provider API key (OpenAI, Anthropic, etc.)","Python 3.9+","Tool definitions (functions or APIs that agents can call)","Prompt templates for agent reasoning"],"input_types":["user tasks or goals (text strings)","tool definitions (function signatures, descriptions)","RAG pipeline configuration","optional conversation history"],"output_types":["agent reasoning trace (thought process, tool calls)","final answers or action results","tool call results and intermediate outputs","execution metadata (steps, latency, cost)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathwaycom--llm-app__cap_12","uri":"capability://tool.use.integration.http.api.exposure.with.fastapi.and.streamlit.ui.deployment","name":"http api exposure with fastapi and streamlit ui deployment","description":"Pathway's llm-app provides built-in HTTP API exposure through FastAPI, enabling RAG pipelines to be consumed by web applications, mobile clients, and third-party integrations. The system also includes Streamlit UI templates for rapid prototyping and user-facing applications, handling request routing, response formatting, error handling, and concurrent request management without additional infrastructure.","intents":["I want to expose my RAG pipeline as a REST API for web/mobile clients","I need a quick user interface to test and demo my RAG system","I want to integrate my RAG pipeline into existing applications via HTTP endpoints"],"best_for":["Teams building production RAG APIs for web/mobile consumption","Developers prototyping RAG systems with quick UI feedback","Organizations integrating RAG into existing application stacks"],"limitations":["FastAPI server requires Python runtime; no native compiled deployment option","Streamlit UI is suitable for prototyping but not production-grade (limited customization, performance)","Concurrent request handling requires careful tuning of worker processes and connection pooling","No built-in authentication or rate limiting; requires additional middleware for production security","Response streaming (for long-running queries) requires WebSocket support, not standard HTTP"],"requires":["Pathway framework with API and UI modules","FastAPI (for HTTP API)","Streamlit (for UI, optional)","Python 3.9+","HTTP server (uvicorn for FastAPI)","Docker for containerized deployment"],"input_types":["HTTP requests (JSON payloads with queries, filters, metadata)","Streamlit form inputs (text, file uploads, sliders)"],"output_types":["HTTP responses (JSON with answers, citations, metadata)","Streamlit UI components (text, tables, charts)","streaming responses (for long-running queries)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathwaycom--llm-app__cap_13","uri":"capability://automation.workflow.docker.containerization.and.cloud.deployment.with.configuration.driven.scaling","name":"docker containerization and cloud deployment with configuration-driven scaling","description":"Pathway's llm-app provides Docker containerization and cloud deployment templates (AWS, GCP, Azure) that package RAG pipelines with all dependencies, enabling reproducible deployments across environments. The system uses configuration files (docker-compose.yml, Kubernetes manifests) to define resource requirements, scaling policies, and environment-specific settings, allowing teams to deploy from development to production without code changes.","intents":["I want to containerize my RAG pipeline for consistent deployment across environments","I need to scale my RAG system to handle variable load with auto-scaling","I want to deploy my RAG pipeline to cloud platforms (AWS, GCP, Azure) with minimal configuration"],"best_for":["Teams deploying RAG systems to cloud platforms","Organizations requiring reproducible deployments across dev/staging/production","Applications with variable load requiring auto-scaling capabilities"],"limitations":["Container image size is large (1-3GB) due to LLM dependencies; slow to push/pull in bandwidth-constrained environments","GPU support in containers requires NVIDIA Docker runtime; not all cloud providers support GPU containers equally","Stateful components (vector databases, caches) require persistent volumes; managing state across container replicas is complex","Cold start latency for containers with large models is significant (30-60 seconds); not suitable for serverless deployments","Monitoring and logging require additional infrastructure (CloudWatch, Datadog, etc.)"],"requires":["Docker and Docker Compose","Kubernetes (optional, for orchestration)","Cloud provider account (AWS, GCP, Azure) with container registry","Python 3.9+","Configuration files (docker-compose.yml, Kubernetes manifests, or cloud-specific configs)","Optional: GPU support (NVIDIA Docker runtime, cloud GPU instances)"],"input_types":["Dockerfile and docker-compose.yml templates","Environment variables and configuration files","Cloud provider credentials"],"output_types":["Docker images (pushed to container registry)","Deployed containers (running on cloud platforms)","Deployment logs and status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathwaycom--llm-app__cap_2","uri":"capability://search.retrieval.hybrid.vector.and.keyword.indexing.with.efficient.similarity.search","name":"hybrid vector and keyword indexing with efficient similarity search","description":"Pathway's llm-app builds and maintains both vector indices (for semantic similarity) and keyword indices (for exact/BM25 matching) that can be queried independently or combined through hybrid search strategies. The system uses configurable vector databases (Qdrant, Weaviate, or in-memory indices) and supports multiple retrieval methods (top-k similarity, MMR diversity, keyword filtering) to balance relevance and diversity in retrieved context.","intents":["I want to retrieve documents using both semantic similarity and keyword matching to improve relevance","I need to support exact phrase searches alongside semantic queries","I want to filter retrieved results by metadata (date, source, category) while maintaining semantic ranking"],"best_for":["Enterprise search applications requiring high precision (legal, medical, financial documents)","Teams building multi-modal RAG systems combining text and structured metadata","Applications where users expect both semantic and keyword search capabilities"],"limitations":["Maintaining dual indices (vector + keyword) increases storage overhead by 30-50% compared to vector-only indexing","Hybrid search query planning adds ~50-100ms latency per query for combining results from multiple indices","Vector database selection is not easily swappable — switching from Qdrant to Weaviate requires re-indexing","No built-in cross-lingual search — keyword indices are language-specific"],"requires":["Pathway framework with indexing modules","Vector database (Qdrant, Weaviate, or in-memory for development)","Embedding model for vector index population","Python 3.9+","Configuration specifying index_type (vector, hybrid, keyword) in app.yaml"],"input_types":["document chunks with embeddings","document metadata (source, date, category, tags)","user queries (text strings)","filter specifications (metadata predicates)"],"output_types":["ranked list of document chunks with relevance scores","retrieval metadata (index used, score components, match type)","filtered result sets with applied constraints"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathwaycom--llm-app__cap_3","uri":"capability://text.generation.language.llm.agnostic.response.generation.with.multi.provider.support","name":"llm-agnostic response generation with multi-provider support","description":"Pathway's llm-app abstracts LLM provider selection (OpenAI, Mistral, Anthropic, local models via Ollama) through a unified interface, allowing developers to swap providers through configuration without code changes. The system manages prompt templating, context injection from retrieved documents, and response streaming, supporting both synchronous and asynchronous LLM calls with configurable retry logic and timeout handling.","intents":["I want to switch between OpenAI GPT-4 and Mistral without changing my application code","I need to use a local LLM (Ollama) for privacy while maintaining the same pipeline interface","I want to implement fallback logic (try OpenAI, fall back to Mistral if rate-limited)"],"best_for":["Teams evaluating multiple LLM providers for cost/performance tradeoffs","Organizations with privacy requirements preventing cloud LLM usage","Developers building multi-tenant systems where LLM choice varies by customer"],"limitations":["Provider-specific features (function calling, vision capabilities) are not uniformly abstracted — some providers require custom prompt engineering","Response streaming behavior varies by provider; some providers have higher latency or different token counting","No built-in cost tracking or usage monitoring across providers","Local LLM inference (Ollama) requires GPU resources and has significantly higher latency than cloud APIs"],"requires":["Pathway framework with LLM integration modules","API key for chosen LLM provider (OpenAI, Mistral, Anthropic, etc.)","For local models: Ollama installed and running, GPU with 8GB+ VRAM","Python 3.9+","app.yaml configuration specifying llm_provider and model_name"],"input_types":["user queries (text strings)","retrieved document context (text chunks)","system prompts and prompt templates","conversation history (for multi-turn interactions)"],"output_types":["generated text responses","token usage metadata (input tokens, output tokens, cost estimates)","streaming response chunks (for real-time UI updates)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathwaycom--llm-app__cap_4","uri":"capability://memory.knowledge.question.answering.rag.pipeline.with.context.aware.retrieval.and.generation","name":"question-answering rag pipeline with context-aware retrieval and generation","description":"Pathway's basic QA RAG template implements an end-to-end pipeline that processes user queries, retrieves relevant document context using hybrid search, and generates answers using an LLM with injected context. The pipeline includes query preprocessing (optional rewriting), context ranking, and response formatting, all orchestrated through Pathway's dataflow engine to handle concurrent requests and maintain state across multiple queries.","intents":["I want to build a chatbot that answers questions based on my company's documentation","I need a production-ready QA system that retrieves context and generates answers in under 2 seconds","I want to expose my RAG pipeline as an HTTP API for web/mobile clients"],"best_for":["Teams building internal knowledge base chatbots","Organizations deploying customer-facing Q&A systems","Developers prototyping RAG applications quickly using templates"],"limitations":["No multi-turn conversation memory — each query is treated independently; building stateful conversations requires external session management","Context window limitations of LLMs (4K-128K tokens) constrain the amount of retrieved context; large document collections may require aggressive filtering","No built-in answer validation or confidence scoring — hallucinations are possible if retrieved context is insufficient","Query rewriting (if enabled) adds latency and requires careful prompt engineering to avoid over-transforming user intent"],"requires":["Pathway framework with RAG pipeline templates","Indexed document collection (via document ingestion capability)","LLM provider API key (OpenAI, Mistral, etc.)","Python 3.9+","Docker for containerized deployment","HTTP server (FastAPI or Streamlit for UI)"],"input_types":["user questions (text strings)","optional conversation context (previous Q&A pairs)","optional metadata filters (date range, document source)"],"output_types":["generated answers (text strings)","source citations (document references with chunk IDs)","confidence metadata (retrieval scores, token usage)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathwaycom--llm-app__cap_5","uri":"capability://planning.reasoning.adaptive.rag.with.query.routing.and.dynamic.context.selection","name":"adaptive rag with query routing and dynamic context selection","description":"Pathway's adaptive RAG template implements intelligent query routing that classifies incoming questions and selects appropriate retrieval strategies (dense retrieval, sparse retrieval, knowledge graph traversal, or direct LLM reasoning) based on query type. The system uses configurable routing logic (rule-based or LLM-based classification) to optimize retrieval quality and latency by avoiding unnecessary context retrieval for simple factual questions or routing complex reasoning to specialized sub-pipelines.","intents":["I want my RAG system to route simple factual questions directly to the LLM without retrieval overhead","I need to handle different question types (factual, reasoning, multi-hop) with specialized retrieval strategies","I want to reduce latency and cost by avoiding expensive retrieval for questions answerable without context"],"best_for":["High-volume QA systems where latency and cost optimization are critical","Applications with diverse question types requiring different retrieval approaches","Teams building intelligent assistants that adapt retrieval strategy to query complexity"],"limitations":["Query classification adds 100-300ms latency per request; routing overhead may exceed savings for simple questions","Requires careful tuning of routing rules to avoid misclassification (e.g., routing a complex question as simple)","LLM-based routing (using an LLM to classify queries) doubles the LLM call count and cost","No built-in evaluation framework for measuring routing accuracy or retrieval strategy effectiveness"],"requires":["Pathway framework with adaptive RAG template","Indexed document collection","LLM provider API key","Python 3.9+","Configuration specifying routing_strategy (rule-based or llm-based) and routing_rules in app.yaml","Optional: knowledge graph or specialized indices for advanced routing"],"input_types":["user questions (text strings)","optional question metadata (user profile, context)","routing rules or classification model"],"output_types":["routing decision (selected retrieval strategy)","retrieved context (if applicable)","generated answers","routing metadata (confidence, strategy used)"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathwaycom--llm-app__cap_6","uri":"capability://memory.knowledge.private.rag.with.local.llms.and.on.premise.data.isolation","name":"private rag with local llms and on-premise data isolation","description":"Pathway's private RAG template enables fully on-premise RAG deployments using local LLMs (Ollama, LLaMA, Mistral) and local vector databases (Qdrant, Weaviate), ensuring no data leaves the organization's infrastructure. The system handles document ingestion, indexing, and inference entirely within a containerized environment, supporting air-gapped deployments and compliance-heavy industries (healthcare, finance, government) where cloud LLM usage is prohibited.","intents":["I need to build a RAG system that never sends data to cloud LLM providers for compliance reasons","I want to deploy a knowledge base chatbot on-premise with no external API dependencies","I need to ensure my proprietary documents never leave our data center"],"best_for":["Healthcare organizations subject to HIPAA or similar data residency requirements","Financial institutions with strict data governance policies","Government agencies and defense contractors requiring air-gapped systems","Organizations with proprietary data too sensitive for cloud processing"],"limitations":["Local LLM inference is significantly slower than cloud APIs (5-10x latency) due to GPU constraints","Smaller local models (7B-13B parameters) have lower quality than cloud models (GPT-4, Claude); hallucination rates are higher","Requires substantial GPU infrastructure (A100, H100) for reasonable throughput; CPU-only inference is impractical","No automatic updates to LLM models; manual model management and version control required","Operational complexity increases significantly — requires managing containerized infrastructure, GPU drivers, and model serving"],"requires":["Pathway framework with private RAG template","Docker and Docker Compose for containerization","GPU hardware (NVIDIA A100/H100 recommended; 8GB+ VRAM minimum)","CUDA 11.8+ or Metal (for Apple Silicon)","Local LLM (Ollama with LLaMA, Mistral, or similar)","Local vector database (Qdrant or Weaviate)","Python 3.9+","Network isolation (air-gapped or VPN-only access)"],"input_types":["local document files (PDF, DOCX, markdown)","local data sources (file systems, PostgreSQL, etc.)","user queries (text strings)"],"output_types":["generated answers (text strings)","source citations","inference metadata (latency, token usage)"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathwaycom--llm-app__cap_7","uri":"capability://image.visual.multimodal.rag.with.image.understanding.and.visual.document.processing","name":"multimodal rag with image understanding and visual document processing","description":"Pathway's multimodal RAG template extends RAG to handle images, PDFs with embedded images, and visual documents using vision-capable LLMs (GPT-4V, Claude 3 Vision). The system extracts images from documents, generates image embeddings (using CLIP or similar models), indexes images alongside text chunks, and retrieves both text and visual content based on user queries, enabling QA over documents with charts, diagrams, and photographs.","intents":["I want to build a QA system over documents containing charts, diagrams, and images","I need to retrieve and answer questions about visual content in my document collection","I want to use vision-capable LLMs to understand images and generate answers based on visual context"],"best_for":["Organizations with document collections heavy in visual content (annual reports, research papers, technical manuals)","Teams building document analysis systems for engineering, architecture, or design documents","Applications requiring understanding of charts, graphs, and infographics"],"limitations":["Vision-capable LLMs (GPT-4V, Claude 3 Vision) are significantly more expensive than text-only models (3-5x cost per token)","Image embedding models (CLIP) have lower quality than text embeddings; image-to-text retrieval is less reliable","Processing large PDFs with many images increases latency significantly (100-500ms per image for vision LLM processing)","No built-in image segmentation or layout analysis — complex documents with multiple images per page require careful preprocessing","Vision LLM APIs have lower rate limits and longer processing times than text APIs"],"requires":["Pathway framework with multimodal RAG template","Vision-capable LLM API (OpenAI GPT-4V, Anthropic Claude 3 Vision, or local vision model)","Image embedding model (CLIP, or similar)","Document parser supporting image extraction (PyPDF2, pdfplumber, or similar)","Python 3.9+","GPU for local image embedding (optional but recommended)","Sufficient API quota for vision LLM calls"],"input_types":["documents with embedded images (PDF, DOCX)","standalone images (PNG, JPG)","user queries (text strings, optional image references)","image metadata (captions, alt text)"],"output_types":["retrieved text chunks and images","generated answers incorporating visual understanding","image descriptions and analysis","multimodal source citations"],"categories":["image-visual","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathwaycom--llm-app__cap_8","uri":"capability://search.retrieval.slides.ai.search.with.presentation.content.indexing.and.retrieval","name":"slides ai search with presentation content indexing and retrieval","description":"Pathway's specialized slides search template indexes presentation files (PowerPoint, Google Slides) by extracting slide content (text, images, speaker notes) and building searchable indices. The system handles slide-specific metadata (slide number, section, speaker notes) and enables semantic search across presentations, allowing users to find relevant slides and generate summaries or answers based on presentation content.","intents":["I want to search across hundreds of presentation files to find relevant slides","I need to extract key points from presentations and generate summaries","I want to answer questions based on presentation content without manually reviewing slides"],"best_for":["Organizations with large presentation libraries (training, sales, research)","Teams conducting competitive analysis or market research across multiple presentations","Educational institutions indexing lecture slides for student discovery"],"limitations":["Presentation parsing is format-specific; Google Slides requires API access, PowerPoint requires python-pptx library","Speaker notes are often missing or incomplete, reducing context quality","Slide images (charts, diagrams) require vision LLM processing for understanding, adding cost and latency","Slide-specific metadata (section, speaker) requires custom extraction logic","No built-in handling of animations or slide transitions that convey information"],"requires":["Pathway framework with slides search template","Presentation files (PowerPoint, Google Slides, or PDF exports)","For Google Slides: Google Slides API credentials","For PowerPoint: python-pptx library","Python 3.9+","Optional: Vision LLM for understanding slide images"],"input_types":["presentation files (PPTX, ODPS, PDF)","Google Slides URLs","user search queries (text strings)"],"output_types":["retrieved slides with metadata (slide number, section, content)","slide summaries","answers based on slide content","slide-specific citations"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-pathwaycom--llm-app__cap_9","uri":"capability://data.processing.analysis.unstructured.data.to.sql.transformation.with.schema.aware.extraction","name":"unstructured data to sql transformation with schema-aware extraction","description":"Pathway's specialized unstructured-to-SQL template uses LLMs to extract structured data from unstructured documents (emails, PDFs, text files) and map it to relational database schemas. The system handles schema validation, type coercion, and error handling, enabling bulk ingestion of unstructured data into SQL databases while maintaining referential integrity and data quality constraints.","intents":["I want to extract structured data from unstructured documents and load it into my database","I need to convert email chains, PDFs, and text files into structured records with validation","I want to automate data entry from unstructured sources without manual transcription"],"best_for":["Organizations with high-volume unstructured data requiring structured storage (invoices, contracts, forms)","Teams automating data entry from documents into relational databases","Businesses processing documents with variable formats (emails, PDFs, scanned forms)"],"limitations":["LLM-based extraction is not 100% accurate; hallucinations and misinterpretations require validation and correction","Schema mismatch errors require manual intervention; no automatic schema evolution or conflict resolution","Extraction cost scales with document volume; processing thousands of documents becomes expensive","Complex nested structures (hierarchical data, many-to-many relationships) are difficult to extract reliably","No built-in duplicate detection or deduplication across extracted records"],"requires":["Pathway framework with unstructured-to-SQL template","LLM provider API key (OpenAI, Mistral, etc.)","Target SQL database (PostgreSQL, MySQL, etc.) with defined schema","Python 3.9+","Schema definition (SQL DDL or JSON schema)","Unstructured documents (PDF, email, text files)"],"input_types":["unstructured documents (PDF, email, text, images)","target database schema (SQL DDL)","extraction rules or prompts","validation constraints"],"output_types":["extracted structured records (JSON or SQL rows)","validation results (success/failure per record)","error logs and correction suggestions","database insert statements"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["Pathway framework installed (Python 3.9+)","API credentials for target data sources (Google Drive API key, SharePoint tenant credentials, S3 access keys, etc.)","Network connectivity to source systems","Docker for containerized deployment of connectors","Pathway framework with embedding module","Python 3.9+","For cloud embeddings: API key for OpenAI or Hugging Face Inference API","For local embeddings: GPU (CUDA/Metal) or CPU with 4GB+ RAM","app.yaml configuration file with text_splitter and embedding_model sections","Pathway framework with Drive Alert template"],"failure_modes":["Connector availability varies by source — not all cloud storage providers have native connectors","Real-time sync adds operational complexity for managing connection credentials and monitoring connector health","Large-scale document changes (millions of files) may require tuning of polling intervals to avoid API rate limits","Semantic chunking (e.g., sentence-boundary aware) requires language-specific tokenizers and adds ~50-200ms per document","Local embedding models require GPU resources for reasonable throughput; 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